The present invention relates to systems that employ electronic program guides (EPGs) to assist media users in managing a large number of media-content choices, for example, television programming, chatrooms, on-demand video media files, audio, etc. More specifically, the invention relates to such systems that provide “intelligence”, such as an ability to suggest choices and an ability to take actions, for example to record a program, on the user's behalf based on the user's preferences.
A common element among conventional Electronic Program Guide (EPG) systems is their ability to display listings of programs for many available channels. The listings may be generated locally and displayed interactively. The listings are commonly arranged in a grid, with each row representing a particular broadcast or cable channel, such as ABC, PBS, or ESPN and each column of the grid representing a particular time slot, such as 4:00 p.m. to 4:30 p.m. Multiple rows and multiple columns can be displayed on the screen simultaneously. The various scheduled programs or shows are arranged within the rows and columns, indicating the channels and times at which they can be found. The grid can be scrolled vertically so that a viewer can scan through different channels within a given interval of time. The grid may also be scrolled horizontally (panned) to change the time interval displayed.
Data regarding available programs may be received by a cable system or telephone line as a set of data records. Each available program may have a single corresponding data record containing information about the program such as its channel, its starting and ending times, its title, names of starring actors, whether closed-captioning and stereo are available, and perhaps a brief description of the program. It is not difficult to format a grid such as described above from these types of data records. The data spanning a period (e.g., two weeks) are typically formatted once at the server (e.g., the cable system's head-end) and broadcast repeatedly and continuously to the homes served by the cable system. Alternatively, the data may be downloaded via phone line, or other network, either on-demand or on a predetermined schedule.
An EPG system can run on a device with a user interface (hereinafter a “user interface device”), which can be a set-top box (STB), a general purpose computer, an embedded system, a controller within the television, or the server of a communications network or Internet server. The user interface device is connected to the TV to generate displays and receive input from the user. When scrolling to a new column or row, the user interface device may retrieve appropriate information from a stored database (in the user interface device or elsewhere) regarding the programming information that needs to be presented for the new row or column. For instance, when scrolling to a new column, programs falling within a new time slot need to be displayed.
Electronic program guides (EPGs) promise to make more manageable, the task of choosing from among the myriad television and other media viewing choices. An interactive application of EPGs builds a user-preference database and uses the preference data to make suggestions, filter current or future programming information to simplify the job of choosing, or even make choices on behalf of the user. For example, the system could record a program without a specific request from the user or highlight choices that it recommends.
A first type of device for building the preference database is a passive one from the standpoint of the user. The user merely makes choices in the normal fashion from raw EPG data and the system gradually builds a personal preference database by extracting a model of the user's behavior from the choices. It then uses the model to make predictions about what the user would prefer to watch in the future. This extraction process can follow simple algorithms, such as identifying apparent favorites by detecting repeated requests for the same item, or it can be a sophisticated machine-learning process such as a decision-tree technique with a large number of inputs (degrees of freedom). Such models, generally speaking, look for patterns in the user's interaction behavior (i.e., interaction with the user-interface (UI) for making selections).
One straightforward and fairly robust technique for extracting useful information from the user's pattern of watching is to generate a table of feature-value counts. An example of a feature is the “time of day” and a corresponding value could be “morning.” When a choice is made, the counts of the feature-values characterizing that choice are incremented. Usually, a given choice will have many feature-values. A set of negative choices may also be generated by selecting a subset of shows (optionally, at the same time) from which the choice was discriminated. Their respective feature-value counts will be decremented (or a count for shows not watched incremented). These data are sent to a Bayesian predictor which uses the counts as weights to feature-counts characterizing candidates to predict the probability that a candidate will be preferred by a user. This type of profiling mechanism is described in U.S. patent application Ser. No. 09/498,271, filed Feb. 4, 2000 for BAYESIAN TV SHOW RECOMMENDER, the entirety of which is hereby incorporated by reference as if fully set forth herein. A rule-based recommender in this same class of systems, which build profiles passively from observations of user behavior, is also described in the PCT application, WO 99/01984 published Jan. 14, 1999 for INTELLIGENT ELECTRONIC PROGRAM GUIDE.
Another example of the first type is MbTV, a system that learns viewers' television watching preferences by monitoring their viewing patterns. MbTV operates transparently and builds a profile of a viewer's tastes. This profile is used to provide services, for example, recommending television programs the viewer might be interested in watching. MbTV learns about each of its viewer's tastes and uses what it learns to recommend upcoming programs. MbTV can help viewers schedule their television watching time by alerting them to desirable upcoming programs, and with the addition of a storage device, automatically record these programs when the viewer is absent.
MbTV has a Preference Determination Engine and a Storage Management Engine. These are used to facilitate time-shifted television. MbTV can automatically record, rather than simply suggest, desirable programming. MbTV's Storage Management Engine tries to insure that the storage device has the optimal contents. This process involves tracking which recorded programs have been viewed (completely or partially), and which are ignored. Viewers can “lock” recorded programs for future viewing in order to prevent deletion. The ways in which viewers handle program suggestions or recorded content provides additional feedback to MbTV's preference engine which uses this information to refine future decisions.
MbTV will reserve a portion of the recording space to represent each “constituent interest.” These “interests” may translate into different family members or could represent different taste categories. Though MbTV does not require user intervention, it is customizable by those that want to fine-tune its capabilities. Viewers can influence the “storage budget” for different types of programs. For example, a viewer might indicate that, though the children watch the majority of television in a household, no more than 25% of the recording space should be consumed by children's programs.
A second type of device is more active. It permits the user to specify likes or dislikes by grading features. These can be scoring feature-value pairs (a weight for the feature plus a value; e.g., weight=importance of feature and value the preferred or disfavored value) or some other rule-specification such as favorite programs, combinations of feature-value pairs like “I like documentaries, but not on Thursday which is the night when the gang comes over.” For example, the user can indicate, through a user interface, that dramas and action movies are favored and that certain actors are disfavored. These criteria can then be applied to predict which, from among a set of programs, would be preferred by the user.
As an example of the second type of system, one EP application (EP 0854645A2), describes a system that enables a user to enter generic preferences such as a preferred program category, for example, sitcom, dramatic series, old movies, etc. The application also describes preference templates in which preference profiles can be selected, for example, one for children aged 10-12, another for teenage girls, another for airplane hobbyists, etc.
A third type of system allows users to rank programs in some fashion. For example, currently, TIVO® permits user's to give a show up to three thumbs up or up to three thumbs down. This information is similar in some ways to the second type of system, except that it permits a finer degree of resolution to the weighting given to the feature-value pairs that can be achieved and similar to the first type except the expression of user taste in this context is more explicit. (Note, this is not an admission that the Bayesian technology discussed in U.S. patent application Ser. No. 09/498,271 combined with user-ranking, as in the third type of system, is in the prior art.)
A PCT application (WO 97/4924 entitled System and Method for Using Television Schedule Information) is an example of the third type. It describes a system in which a user can navigate through an electronic program guide displayed in the usual grid fashion and select various programs. At each point, he/she may be doing any of various described tasks, including, selecting a program for recording or viewing, scheduling a reminder to watch a program, and selecting a program to designate as a favorite. Designating a program as a favorite is for the purpose, presumably, to implement a fixed rule such as: “Always display the option of watching this show” or to implement a recurring reminder. The purpose of designating favorites is not clearly described in the application. However, more importantly, for purposes of creating a preference database, when the user selects a program to designate as a favorite, she/he may be provided with the option of indicating the reason it is a favorite. The reason is indicated in the same fashion as other explicit criteria: by defining generic preferences.
The first type of system has the advantage of being easier on the user since the user does not have to provide any explicit data. The user need merely interact with the system. For any of the various machine-learning or predictive methods to be effective, a substantial history of interaction must be available to build a useful preference database. The second and third types have the advantage of providing explicit preference information. The second is reliable, but not perfect as a user may have a hard time abstracting his own preferences to the point of being able to decide which criteria are good discriminators and what weight to give them. The third does not burden the user and probably provides the best quality of information, but can be a burden to generate and still may not contain all the information that can be obtained with the second and also may require information on many shows like the first.